Evaluation of an Algorithmic Approach in Comparison with the Illumigene Assay for Laboratory Diagnosis of Clostridium difficile Infection
Bibliographic record
Abstract
The following three diagnostic algorithms were evaluated in comparison with the Illumigene assay as a stand-alone test for Clostridium difficile detection: glutamate dehydrogenase antigen screen (GDH) followed by toxin A/B antigen testing (Tox A/B) with the cell cytotoxicity assay for discordant specimens (algorithm 1), GDH followed by the Illumigene (algorithm 2), and GDH followed by Tox A/B with the Illumigene for discordant specimens (algorithm 3). A total of 428 stool specimens submitted to three clinical microbiology laboratories in Manitoba, Canada, for C. difficile detection between June 2011 and April 2012 were included in the study. The prevalence of C. difficile in the stool specimens was 14.7% (63/428) based on toxigenic culture (microbiologic reference standard). The sensitivity and specificity of the Illumigene for C. difficile detection were 73.0% and 99.7%, respectively. The corresponding sensitivities and specificities were 65.1% and 100.0% for algorithm 1, 68.3% and 100.0% for algorithm 2, and 69.8% and 100.0% for algorithm 3. Using algorithm 1, a cell cytotoxicity assay was required for toxin detection in 37% of positive tests, prolonging turnaround time. However, the predictive value of a positive test based on a clinical reference standard (all tests positive or cytotoxigenic culture positive and clinical disease on chart review) was slightly higher with algorithm 1 than with the Illumigene assay as a stand-alone test or as part of an algorithm (algorithms 2 and 3). Based on a reduction in turnaround time, simplicity, and acceptable sensitivity and specificity, we recommend algorithm 2 (screening with the GDH antigen test and confirmatory testing with the Illumigene).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".